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Understanding the distribution of wind field parameters over various terrains is important for kinds of applications including condition monitoring of high-speed railways and pollutant dispersion modeling. Based on Empirical Bernstein Copula (EBC), a nonparametric joint distribution model of wind speed and direction adaptable for complex wind field is proposed. Four commonly used parametric models are introduced for comparison, including the angular-linear model, Frank Copula, Gaussian Copula, and the model without considering interdependence. Yearly data measured from wind monitoring stations at 19 sites with various wind fields alongside the Lanzhou-Xinjiang high-speed railway in China is adopted for studying. The EBC model shows an overall best goodness-of-fit with the comprehensive metric value reaching 4.9586 (full score 5). Particularly, the superior performance of the EBC model is retained while the parametric models fail to predict the joint distribution in regions where wind speed and direction are highly dependent. In addition, when dealing with cases that the fitting goodness of marginal distribution is relatively poor (i.e. R 2 = 0.488), a desired accuracy of joint distribution can be obtained with EBC. Further discussion about the optimal parameters and Copula structure of EBC is conducted to reveal reasons for the model showing adaptability to highly variable wind environments. • A nonparametric copula-based model for JPDF of wind speed and direction adaptable for various terrains is proposed. • Performance and robustness of various JPDF models are evaluated at the scale of the whole railway region in China. • The fitting accuracy of the EBC and parametric copulas to model the JPDF of highly dependent wind vector data is compared. • The accuracy of JPDF models is discussed when dealing with cases that the fitting goodness of marginal distribution is poor. [ABSTRACT FROM AUTHOR]

Porcine circovirus type 2 (PCV2) causes infectious diseases in pigs leading to considerable economic losses in the pig industry. To prevent and control PCV2 infections, is important to understand the prevalence and geographical distribution of the virus. We performed the first systematic review and meta-analysis to estimate the prevalence of PCV2 in China. From PubMed, ScienceDirect, Chinese Web of Knowledge, Wanfang, and VIP Chinese Journal, we extracted 53 studies published in China between 2015 and 2019. There were 29,051 samples, 14,230 of which were positive for PCV2. The pooled prevalence of PCV2 was 46.0%, with the highest in Northeastern China (58.1%). The highest prevalence was 86.3% in Xinjiang province. Nursery pigs had the highest prevalence of PCV2 (50.9%), and the serological test detected the highest number of cases (58.5%). PCV2 prevalence was 50.1% in intensive farms and 37.5% in extensive farms. Our findings showed that PCV2 is common throughout China. Effective control measures are necessary to reduce PCV2 infections. • Porcine circovirus type 2 (PCV2) is highly prevalent and uneven distribution in China. • A higher prevalence of PCV2 was detected in Serological test. • Intensive farming model may play an active role in the prevention of PCV2. • This is the first meta-analysis of PCV2 prevalence in China. [ABSTRACT FROM AUTHOR]